UMTS Radio Network Planning: Mastering Cell Coupling for Capacity Optimization
Published on 11. March 2009
X, 186 pages
978-3-8348-9260-7 (ISBN)
Description
The universalmobiletelecommunications system (UMTS) is a technical standard for a third generation (3G) telecommunication system. UMTS provides data rates more than three 1 times higher than its second generation (2G) predecessors. The increased speed en ables, for example, video calls, music downloads, or fast web surfing. The technology is already widely available. In 2007, a total of 166 commercial UMTS radio networks are operational in 66 countries covering all continents (UMTS Forum, 20°7), and there are already more than 100 million UMTS subscribers (3GToday. com, 2007; UMTS Forum, 2006). The market for mobile telecommunication is, however, increasingly competi tive, therefore operators need to invest effectively. In Western Europe, mobile phone penetration has reached 100 % in 2006 and the average revenue per customer is de clining (3G. co. uk, 2007). Besides spectrum license fees, the main cost driver is network infrastructure (Ellingeret al. , 2002). Radio network planning can cut operational and capital expenditure by up to 30 % (Dehghan, 2005). Good radio network planning is difficult for UMTS, because its radio interface is more complex than anything used at mass-market level before (Dehghan, 2005). Con nections are separated via codes; they share the same frequency band and are thus subject to interference. On each link, the system constantly regulates the amount of generated interference to a minimum via a power-control feedback loop.
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Hans-Florian Geerdes
UMTS Radio Network Planning: Mastering Cell Coupling for Capacity Optimization
Book
09/2008
Vieweg+Teubner Verlag
€106.99
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Person
Dr. Hans-Florian Geerdes is a scientist at Zuse Institute Berlin and at the DFG research center MATHEON: Mathematics for Key Technologies. His research focuses on applications of combinatorial optimization in wireless telecommunications.
Content
Radio network modeling and performance evaluation for UMTS.- Interference-coupling complementarity systems.- Expected-interference-coupling estimates for network performance.- Network performance optimization.- Conclusion.
4 Expected-i nterference-coupling estimates for network performance (S. 63-64)
Radio network planning aims at improving the expected network performance, so we are not interested in network performance on a single snapshot, but on the expected performance for random snapshots. The coupling matrices thus have to be considered random variables subject to a probability distribution induced by the distribution on snapshots, and we are interested in the stochastics of the performance indicators. Simulation methods are commonly used for determining mean values of performance indicators, but they are inherently too time consuming for use in heavy-duty optimization tools, therefore, faster approaches are needed.
While Monte Carlo methods can yield an arbitrary accuracy if sufficient time is granted, high (absolute) precision is dispensable for taking intermediate planning decisions. For a successful optimization campaign, the ability to quickly discriminate between design alternatives is paramount. The right decision can be made in short time, if accuracy is sacrificed in a controlled fashion. The practical relevance of quick estimation techniques is apparent from the fact that many commercial software tools advertise fast proprietary evaluation methods besides Monte Carlo simulation (Aircom International ltd, 2007, Cosiro GmbH, 2006, Ericsson AB, 2006, Lustig et al., 2004).
In this chapter, we develop methods for estimating the expected network performance with little computational effort. The basic idea is to calculate approximations to the mean values of capacity-related performance indicators based on the mean coupling matrix. The scheme depends on suitable choices of the performance model and of the random model. With the interference coupling complementarity systems, we have a detailed model that reflects the relations between cells. We restrict the random model on snapshots to exclude shadow fading, calculating with the medians of attenuation (the deterministic path loss component) instead.
The resulting method of expected interferencecoupling with medians of attenuation is tailored to the common representation of planning data in computer software. We complement it with a specialized method that calculates better estimates of the grade of service using second-order moments. Besides the method itself, this chapter contributes the thorough analysis of the expected coupling method and its validation as a suitable tool for network planning. Our investigations comprise analytical and empirical studies.
On the analytical side, we use the new generalized pole equations in a simplified setting, the results explain how the service mix determines the variance of the coupling matrix and thereby the quality of the estimates. In our computational studies, we essentially demonstrate that the method is sufficiently informative for typical applications in network planning. The remainder of this chapter is structured as follows: We introduce the accurate reference method of Monte Carlo simulation in Sec.4.1. We define the expected coupling estimates and analyze their accuracy in Sec. 4.2. Refined estimates for the grade of service are developed in Sec. 4.3. In Sec. 4.4, we conduct extensive computational experiments to analyze the accuracy of our new estimates and assess the validity of perfect load control in realistic settings. We draw conclusions on network modeling and performance evaluation in Sec. 4.5. Related work. Random quantities are often represented by their mean in a first-order approximation, in so far the expected coupling approach is a canonical choice.
Radio network planning aims at improving the expected network performance, so we are not interested in network performance on a single snapshot, but on the expected performance for random snapshots. The coupling matrices thus have to be considered random variables subject to a probability distribution induced by the distribution on snapshots, and we are interested in the stochastics of the performance indicators. Simulation methods are commonly used for determining mean values of performance indicators, but they are inherently too time consuming for use in heavy-duty optimization tools, therefore, faster approaches are needed.
While Monte Carlo methods can yield an arbitrary accuracy if sufficient time is granted, high (absolute) precision is dispensable for taking intermediate planning decisions. For a successful optimization campaign, the ability to quickly discriminate between design alternatives is paramount. The right decision can be made in short time, if accuracy is sacrificed in a controlled fashion. The practical relevance of quick estimation techniques is apparent from the fact that many commercial software tools advertise fast proprietary evaluation methods besides Monte Carlo simulation (Aircom International ltd, 2007, Cosiro GmbH, 2006, Ericsson AB, 2006, Lustig et al., 2004).
In this chapter, we develop methods for estimating the expected network performance with little computational effort. The basic idea is to calculate approximations to the mean values of capacity-related performance indicators based on the mean coupling matrix. The scheme depends on suitable choices of the performance model and of the random model. With the interference coupling complementarity systems, we have a detailed model that reflects the relations between cells. We restrict the random model on snapshots to exclude shadow fading, calculating with the medians of attenuation (the deterministic path loss component) instead.
The resulting method of expected interferencecoupling with medians of attenuation is tailored to the common representation of planning data in computer software. We complement it with a specialized method that calculates better estimates of the grade of service using second-order moments. Besides the method itself, this chapter contributes the thorough analysis of the expected coupling method and its validation as a suitable tool for network planning. Our investigations comprise analytical and empirical studies.
On the analytical side, we use the new generalized pole equations in a simplified setting, the results explain how the service mix determines the variance of the coupling matrix and thereby the quality of the estimates. In our computational studies, we essentially demonstrate that the method is sufficiently informative for typical applications in network planning. The remainder of this chapter is structured as follows: We introduce the accurate reference method of Monte Carlo simulation in Sec.4.1. We define the expected coupling estimates and analyze their accuracy in Sec. 4.2. Refined estimates for the grade of service are developed in Sec. 4.3. In Sec. 4.4, we conduct extensive computational experiments to analyze the accuracy of our new estimates and assess the validity of perfect load control in realistic settings. We draw conclusions on network modeling and performance evaluation in Sec. 4.5. Related work. Random quantities are often represented by their mean in a first-order approximation, in so far the expected coupling approach is a canonical choice.
